In recent years, transportation methods have changed substantially. This change is due in part to a concern over the limited availability of natural resources, a proliferation in personal technology, and a societal shift to adopt more environmentally friendly transportation solutions. These considerations have encouraged the development of a number of new flexible-fuel vehicles, hybrid-electric vehicles, and electric vehicles.
While these vehicles appear to be new they are generally implemented as a number of traditional subsystems that are merely tied to an alternative power source. In fact, the design and construction of the vehicles is limited to standard frame sizes, shapes, materials, and transportation concepts. Among other things, these limitations fail to take advantage of the benefits of new technology, power sources, and support infrastructure.
With recent advancements in lithium-ion battery technology, the role and use of batteries in devices (such as phones, smart-wrist watches, tablet computers, etc.) have extended tremendously. Moreover, with the movement towards green energy resources, improvements in technology have led to extensive use of batteries in electric cars. From this perspective, not only a state of charge (SOC) and state of health (SOH) of an electric car battery are important problems to tackle, but also the battery control/management problem has become a very important issue. The storage capacity of a battery degrades as it consumes and produces power. Within this context, battery control management systems are mainly used to minimize degradation in order to improve the overall life cycle, and endurance of batteries.
Present methods of increasing the life of a battery focus mainly on reducing the battery degradation process. Based on heuristic rules to minimize battery degradation, contemporary methods conduct economic analyses of electric vehicle battery degradation. In some approaches, a model predictive control (MPC) approach has been employed to deal with the degradation as an economic cost based on a linearized system model. Some approaches develop PID controllers, feedback linearization controllers and sliding controllers to control nonlinear batteries to track ancillary services signals and minimize the degradation. However, such approaches rely on linearization techniques and controllers designed for the linearized representative.
There are many contemporary methods dedicated to solving the battery control problem ranging from model predictive control to optimal real-time control. In such methods, utilized models range from models operating in linear fashion to somewhat complex nonlinear systems and concentrate on the battery control/management problem with linear control routines.
Portions of the disclosure relate to the following materials which are each incorporated herein by reference for all that they disclose: Arthur Earl Bryson, Applied optimal control: optimization, estimation and control, CRC Press, 1975 (“Bryson”); James F Manwell and Jon G McGowan, Lead acid battery storage model for hybrid energy systems, Solar Energy, 50(5), 399-405, 1993 (“Manwell”); Johanna L Mathieu and Joshua A Taylor, Controlling nonlinear batteries for power systems: Trading off performance and battery life, In 2016 Power Systems Computation Conference, pages 1-7, IEEE, 2016 (“Mathieu”); and Toshiyuki Ohtsuka, Time-variant receding-horizon control of nonlinear systems, Journal of Guidance, Control, and Dynamics, 21(1):174-176, 1998 (“Ohtsuka”).